House pricing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mw2VBrBcgvGa"
   },
   "source": [
    "In this exercise you'll try to build a neural network that predicts the price of a house according to a simple formula.\n",
    "\n",
    "So, imagine if house pricing was as easy as a house costs 50k + 50k per bedroom, so that a 1 bedroom house costs 100k, a 2 bedroom house costs 150k etc.\n",
    "\n",
    "How would you create a neural network that learns this relationship so that it would predict a 7 bedroom house as costing close to 400k etc.\n",
    "\n",
    "Hint: Your network might work better if you scale the house price down. You don't have to give the answer 400...it might be better to create something that predicts the number 4, and then your answer is in the 'hundreds of thousands' etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PUNO2E6SeURH"
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: house_model\n",
    "def house_model(y_new):\n",
    "    xs = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], dtype = float)\n",
    "    ys = np.array([100, 150, 200, 250, 300, 350], dtype = float)\n",
    "    model = tf.keras.Sequential([keras.layers.Dense(units = 1, input_shape = [1])])\n",
    "    model.compile(optimizer = 'sgd', loss = 'mean_squared_error')\n",
    "    model.fit(xs, ys/100, epochs = 1000)\n",
    "    return model.predict(y_new)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0616 07:27:56.495256 139974479771456 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 944/1000\n",
      "6/6 [==============================] - 0s 330us/sample - loss: 2.7484e-08\n",
      "Epoch 945/1000\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 2.7289e-08\n",
      "Epoch 946/1000\n",
      "6/6 [==============================] - 0s 362us/sample - loss: 2.7108e-08\n",
      "Epoch 947/1000\n",
      "6/6 [==============================] - 0s 338us/sample - loss: 2.6909e-08\n",
      "Epoch 948/1000\n",
      "6/6 [==============================] - 0s 509us/sample - loss: 2.6698e-08\n",
      "Epoch 949/1000\n",
      "6/6 [==============================] - 0s 270us/sample - loss: 2.6504e-08\n",
      "Epoch 950/1000\n",
      "6/6 [==============================] - 0s 321us/sample - loss: 2.6321e-08\n",
      "Epoch 951/1000\n",
      "6/6 [==============================] - 0s 239us/sample - loss: 2.6124e-08\n",
      "Epoch 952/1000\n",
      "6/6 [==============================] - 0s 350us/sample - loss: 2.5912e-08\n",
      "Epoch 953/1000\n",
      "6/6 [==============================] - 0s 13ms/sample - loss: 2.5747e-08\n",
      "Epoch 954/1000\n",
      "6/6 [==============================] - 0s 435us/sample - loss: 2.5562e-08\n",
      "Epoch 955/1000\n",
      "6/6 [==============================] - 0s 312us/sample - loss: 2.5364e-08\n",
      "Epoch 956/1000\n",
      "6/6 [==============================] - 0s 384us/sample - loss: 2.5191e-08\n",
      "Epoch 957/1000\n",
      "6/6 [==============================] - 0s 306us/sample - loss: 2.5015e-08\n",
      "Epoch 958/1000\n",
      "6/6 [==============================] - 0s 602us/sample - loss: 2.4814e-08\n",
      "Epoch 959/1000\n",
      "6/6 [==============================] - 0s 302us/sample - loss: 2.4651e-08\n",
      "Epoch 960/1000\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 2.4465e-08\n",
      "Epoch 961/1000\n",
      "6/6 [==============================] - 0s 356us/sample - loss: 2.4300e-08\n",
      "Epoch 962/1000\n",
      "6/6 [==============================] - 0s 251us/sample - loss: 2.4100e-08\n",
      "Epoch 963/1000\n",
      "6/6 [==============================] - 0s 290us/sample - loss: 2.3935e-08\n",
      "Epoch 964/1000\n",
      "6/6 [==============================] - 0s 264us/sample - loss: 2.3738e-08\n",
      "Epoch 965/1000\n",
      "6/6 [==============================] - 0s 340us/sample - loss: 2.3573e-08\n",
      "Epoch 966/1000\n",
      "6/6 [==============================] - 0s 373us/sample - loss: 2.3404e-08\n",
      "Epoch 967/1000\n",
      "6/6 [==============================] - 0s 317us/sample - loss: 2.3235e-08\n",
      "Epoch 968/1000\n",
      "6/6 [==============================] - 0s 472us/sample - loss: 2.3079e-08\n",
      "Epoch 969/1000\n",
      "6/6 [==============================] - 0s 362us/sample - loss: 2.2903e-08\n",
      "Epoch 970/1000\n",
      "6/6 [==============================] - 0s 377us/sample - loss: 2.2737e-08\n",
      "Epoch 971/1000\n",
      "6/6 [==============================] - 0s 311us/sample - loss: 2.2574e-08\n",
      "Epoch 972/1000\n",
      "6/6 [==============================] - 0s 485us/sample - loss: 2.2403e-08\n",
      "Epoch 973/1000\n",
      "6/6 [==============================] - 0s 275us/sample - loss: 2.2242e-08\n",
      "Epoch 974/1000\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 2.2078e-08\n",
      "Epoch 975/1000\n",
      "6/6 [==============================] - 0s 305us/sample - loss: 2.1920e-08\n",
      "Epoch 976/1000\n",
      "6/6 [==============================] - 0s 334us/sample - loss: 2.1760e-08\n",
      "Epoch 977/1000\n",
      "6/6 [==============================] - 0s 375us/sample - loss: 2.1607e-08\n",
      "Epoch 978/1000\n",
      "6/6 [==============================] - 0s 318us/sample - loss: 2.1439e-08\n",
      "Epoch 979/1000\n",
      "6/6 [==============================] - 0s 354us/sample - loss: 2.1295e-08\n",
      "Epoch 980/1000\n",
      "6/6 [==============================] - 0s 501us/sample - loss: 2.1141e-08\n",
      "Epoch 981/1000\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 2.0998e-08\n",
      "Epoch 982/1000\n",
      "6/6 [==============================] - 0s 378us/sample - loss: 2.0842e-08\n",
      "Epoch 983/1000\n",
      "6/6 [==============================] - 0s 273us/sample - loss: 2.0695e-08\n",
      "Epoch 984/1000\n",
      "6/6 [==============================] - 0s 314us/sample - loss: 2.0530e-08\n",
      "Epoch 985/1000\n",
      "6/6 [==============================] - 0s 284us/sample - loss: 2.0393e-08\n",
      "Epoch 986/1000\n",
      "6/6 [==============================] - 0s 228us/sample - loss: 2.0242e-08\n",
      "Epoch 987/1000\n",
      "6/6 [==============================] - 0s 260us/sample - loss: 2.0092e-08\n",
      "Epoch 988/1000\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 1.9946e-08\n",
      "Epoch 989/1000\n",
      "6/6 [==============================] - 0s 364us/sample - loss: 1.9802e-08\n",
      "Epoch 990/1000\n",
      "6/6 [==============================] - 0s 415us/sample - loss: 1.9655e-08\n",
      "Epoch 991/1000\n",
      "6/6 [==============================] - 0s 514us/sample - loss: 1.9496e-08\n",
      "Epoch 992/1000\n",
      "6/6 [==============================] - 0s 345us/sample - loss: 1.9370e-08\n",
      "Epoch 993/1000\n",
      "6/6 [==============================] - 0s 329us/sample - loss: 1.9221e-08\n",
      "Epoch 994/1000\n",
      "6/6 [==============================] - 0s 279us/sample - loss: 1.9089e-08\n",
      "Epoch 995/1000\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 1.8954e-08\n",
      "Epoch 996/1000\n",
      "6/6 [==============================] - 0s 398us/sample - loss: 1.8803e-08\n",
      "Epoch 997/1000\n",
      "6/6 [==============================] - 0s 328us/sample - loss: 1.8672e-08\n",
      "Epoch 998/1000\n",
      "6/6 [==============================] - 0s 372us/sample - loss: 1.8542e-08\n",
      "Epoch 999/1000\n",
      "6/6 [==============================] - 0s 366us/sample - loss: 1.8412e-08\n",
      "Epoch 1000/1000\n",
      "6/6 [==============================] - 0s 288us/sample - loss: 1.8264e-08\n",
      "[3.999805]\n"
     ]
    }
   ],
   "source": [
    "prediction = house_model([7.0])\n",
    "print(prediction)"
   ]
  }
 ],
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